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Peace and war: trajectories of posttraumatic stress disorder symptoms before, during, and after military deployment in Afghanistan.

ABSTRACT: In the study reported here, we examined posttraumatic stress disorder (PTSD) symptoms in 746 Danish soldiers measured on five occasions before, during, and after deployment to Afghanistan. Using latent class growth analysis, we identified six trajectories of change in PTSD symptoms. Two resilient trajectories had low levels across all five times, and a new-onset trajectory started low and showed a marked increase of PTSD symptoms. Three temporary-benefit trajectories, not previously described in the literature, showed decreases in PTSD symptoms during (or immediately after) deployment, followed by increases after return from deployment. Predeployment emotional problems and predeployment traumas, especially childhood adversities, were predictors for inclusion in the nonresilient trajectories, whereas deployment-related stress was not. These findings challenge standard views of PTSD in two ways. First, they show that factors other than immediately preceding stressors are critical for PTSD development, with childhood adversities being central. Second, they demonstrate that the development of PTSD symptoms shows heterogeneity, which indicates the need for multiple measurements to understand PTSD and identify people in need of treatment.

SUBMITTER: Berntsen D

PROVIDER: S-EPMC3893698 | biostudies-literature | 2012 Dec

REPOSITORIES: biostudies-literature

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Peace and war: trajectories of posttraumatic stress disorder symptoms before, during, and after military deployment in Afghanistan.

Berntsen Dorthe D Johannessen Kim B KB Thomsen Yvonne D YD Bertelsen Mette M Hoyle Rick H RH Rubin David C DC

Psychological science 20121105 12

In the study reported here, we examined posttraumatic stress disorder (PTSD) symptoms in 746 Danish soldiers measured on five occasions before, during, and after deployment to Afghanistan. Using latent class growth analysis, we identified six trajectories of change in PTSD symptoms. Two resilient trajectories had low levels across all five times, and a new-onset trajectory started low and showed a marked increase of PTSD symptoms. Three temporary-benefit trajectories, not previously described in ...[more]

PMID: 23129059

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Project description:ImportanceMilitary deployment involves significant risk for life-threatening experiences that can lead to posttraumatic stress disorder (PTSD). Accurate predeployment prediction of PTSD risk may facilitate the development of targeted intervention strategies to enhance resilience.ObjectiveTo develop and validate a machine learning (ML) model to predict postdeployment PTSD.Design, setting, and participantsThis diagnostic/prognostic study included 4771 soldiers from 3 US Army brigade combat teams who completed assessments between January 9, 2012, and May 1, 2014. Predeployment assessments occurred 1 to 2 months before deployment to Afghanistan, and follow-up assessments occurred approximately 3 and 9 months post deployment. Machine learning models to predict postdeployment PTSD were developed in the first 2 recruited cohorts using as many as 801 predeployment predictors from comprehensive self-report assessments. In the development phase, cross-validated performance metrics and predictor parsimony were considered to select an optimal model. Next, the selected model's performance was evaluated with area under the receiver operating characteristics curve and expected calibration error in a temporally and geographically distinct cohort. Data analyses were performed from August 1 to November 30, 2022.Main outcomes and measuresPosttraumatic stress disorder diagnosis was assessed by clinically calibrated self-report measures. Participants were weighted in all analyses to address potential biases related to cohort selection and follow-up nonresponse.ResultsThis study included 4771 participants (mean [SD] age, 26.9 [6.2] years), 4440 (94.7%) of whom were men. In terms of race and ethnicity, 144 participants (2.8%) identified as American Indian or Alaska Native, 242 (4.8%) as Asian, 556 (13.3%) as Black or African American, 885 (18.3%) as Hispanic, 106 (2.1%) as Native Hawaiian or other Pacific Islander, 3474 (72.2%) as White, and 430 (8.9%) as other or unknown race or ethnicity; participants could identify as of more than 1 race or ethnicity. A total of 746 participants (15.4%) met PTSD criteria post deployment. In the development phase, models had comparable performance (log loss range, 0.372-0.375; area under the curve range, 0.75-0.76). A gradient-boosting machine with 58 core predictors was selected over an elastic net with 196 predictors and a stacked ensemble of ML models with 801 predictors. In the independent test cohort, the gradient-boosting machine had an area under the curve of 0.74 (95% CI, 0.71-0.77) and low expected calibration error of 0.032 (95% CI, 0.020-0.046). Approximately one-third of participants with the highest risk accounted for 62.4% (95% CI, 56.5%-67.9%) of the PTSD cases. Core predictors cut across 17 distinct domains: stressful experiences, social network, substance use, childhood or adolescence, unit experiences, health, injuries, irritability or anger, personality, emotional problems, resilience, treatment, anxiety, attention or concentration, family history, mood, and religion.Conclusions and relevanceIn this diagnostic/prognostic study of US Army soldiers, an ML model was developed to predict postdeployment PTSD risk with self-reported information collected before deployment. The optimal model showed good performance in a temporally and geographically distinct validation sample. These results indicate that predeployment stratification of PTSD risk is feasible and may facilitate the development of targeted prevention and early intervention strategies.

Dataset Information

Peace and war: trajectories of posttraumatic stress disorder symptoms before, during, and after military deployment in Afghanistan.

Publications

Peace and war: trajectories of posttraumatic stress disorder symptoms before, during, and after military deployment in Afghanistan.

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